Literature Review on Feature Selection Methods for High-Dimensional Data

Abstract

selection is a process of removing the redundant and the irrelevant features from a dataset to improve the performance of the machine learning algorithms. The feature selection is also known as variable selection or attribute selection. The features are also known as variables or attributes. The machine learning algorithms can be roughly classified into two categories one is supervised learning algorithm and another one is unsupervised learning algorithm. The supervised learning algorithms learn the labeled data and construct learning models that are known as classifiers. The classifiers are employed for classification or prediction to identify or predict the class-label of the unlabeled data. The unsupervised learning algorithms lean the unlabeled data and construct the learning models that known as clustering models. The clustering models are employed to cluster or categorize the given data for predicting or identifying their group or cluster. Mostly, the feature selections are employed for the supervised learning algorithms since they suffered by the high-dimensional space. Therefore, this paper presents a complete literature review on various feature selection methods for high-dimensional data.